Simple CJS model integrated with a growth in weight model to get phi, p, and growth estimates to compare with Neural Network CMR/growth model
In all the models below, 1 = not observed and 2 = observed in the input encounter data.
Encounter data are available here in the eh.csv file. Weight data are in weight.csv
Growth rate (gr) model structure:
gr(t,i) <- grInt(t,i)
Model code is in ./models/cmrNN_OB/tt_growth/modelCMR_tt_growth_NN_OB_functionsToSource.R
Model is run ‘by hand’ in ./models/cmrNN_OB/tt_growth/modelCMR_tt_growth_NN_OB_makeFile.R
Not using this: Targets are loaded in R/modelCMR_tt_growth_NN_OB.R and saved as modelCMR_tt_growth_NN_OB_target
Model runs:
library(targets)
# Following https://oliviergimenez.github.io/bayesian-cr-workshop/worksheets/4_demo.html
#
modelNum = 1 # growth only
dummy=2
#load('./models/cmrNN_OB/tt_growth/runsOut/tt_growth_NN_OB_mostRecent.RData')
load(paste0('./models/cmrNN_OB/tt_growth/runsOut/tt_growth_NN_OB_model', modelNum, '_mostRecent.RData'))
out_NN_OBmod1 <- d
#Input data
eh <- tar_read(eh_OB_2002_2014_target)In the model code, a value of 1 for z or in gamma or omega signifies the individual is alive and a value of 2 signifies the individual is dead. y[i,j] is the observed data (encounter history file).
out_NN_OBmod1$modelCode{
for (i in 1:N) {
weight[i, first[i]] ~ dnorm(meanWeight_AIS[first[i]],
sd = 2)
weightDATA[i, first[i]] ~ dnorm(weight[i, first[i]],
sd = 0.2)
for (t in (first[i]):(last[i] - 1)) {
weight[i, t + 1] <- weight[i, t] + gr[i, t] * sampleIntervalDATA[i,
t]/90
weightDATA[i, t + 1] ~ dnorm(weight[i, t + 1], sd = 0.2)
}
}
for (i in 1:N) {
for (t in first[i]:(last[i] - 1)) {
gr[i, t] ~ dnorm(grIntT[t], sd = 0.5)
}
}
for (t in 1:(T - 1)) {
grIntT[t] ~ dnorm(0, sd = 1000)
}
}| nIter | nBurnin | nChains | thinRate |
|---|---|---|---|
| 4000 | 2000 | 2 | 5 |
[1] “Run time = 32.138 mins”
priors <- rnorm(out_NN_OBmod1$runData$nIter * out_NN_OBmod1$runData$nChains, 0, 1000)
MCMCtrace(object = out_NN_OBmod1$mcmc,
#ISB = FALSE,
#exact = TRUE,
params = c("grIntT"),
pdf = FALSE,
priors = priors)
MCMCplot(object = out_NN_OBmod1$mcmc, params = c("grIntT"))
Summary values
#|
(summaryMod1_tt_growth <- MCMCsummary(object = out_NN_OBmod1$mcmc, params = c("grIntT"), round = 3) %>%
rownames_to_column(var = "var")) var mean sd 2.5% 50% 97.5% Rhat n.eff
1 grIntT[1] 1.169 0.028 1.119 1.168 1.227 1.01 197
2 grIntT[2] 1.122 0.022 1.080 1.123 1.165 1.01 168
3 grIntT[3] 9.277 0.028 9.222 9.278 9.329 1.01 91
4 grIntT[4] 3.327 0.023 3.282 3.327 3.374 1.03 212
5 grIntT[5] 0.584 0.033 0.521 0.585 0.647 1.05 135
6 grIntT[6] 1.557 0.036 1.485 1.556 1.622 1.03 134
7 grIntT[7] 17.120 0.045 17.030 17.121 17.212 1.13 59
8 grIntT[8] 7.398 0.059 7.288 7.397 7.509 1.00 86
9 grIntT[9] -1.513 0.080 -1.665 -1.512 -1.333 1.05 39
10 grIntT[10] 4.099 0.083 3.942 4.096 4.257 1.13 43
11 grIntT[11] 22.872 0.120 22.625 22.865 23.101 1.10 21 # To get the mMCMCSummaryRMNA funcion which I adapted to deal with NAs
source('./models/cmrNN_OB/tt_growth/modelCMR_tt_growth_NN_OB_functionsToSource.R')
#summaryMod1_tt_growth_z0 <- MCMCSummaryRMNA(object = out_NN_OBmod1$mcmc, params = c("weightDATA"), round = 3) %>%
summaryMod1_tt_growth_zGR <- MCMCSummaryRMNA(object = out_NN_OBmod1$mcmc, params = c("gr"), round = 3) %>%
rownames_to_column(var = "var") |>
mutate(
ind0 = str_match(var, "([0-9]+), ([0-9]+)")[,2],
t0 = str_match(var, "([0-9]+), ([0-9]+)")[,3],
ind = as.numeric(ind0),
t = as.numeric(t0)
) |>
dplyr::select(-c(t0, ind0))
summaryMod1_tt_growth_zW <- MCMCSummaryRMNA(object = out_NN_OBmod1$mcmc, params = c("weightDATA"), round = 3) %>%
rownames_to_column(var = "var") |>
mutate(
ind0 = str_match(var, "([0-9]+), ([0-9]+)")[,2],
t0 = str_match(var, "([0-9]+), ([0-9]+)")[,3],
ind = as.numeric(ind0),
t = as.numeric(t0)
) |>
dplyr::select(-c(t0, ind0))
summaryMod1_tt_growth_z0 <- summaryMod1_tt_growth_zW |>
left_join(summaryMod1_tt_growth_zGR, by = c("ind", "t"), suffix = c("_weight", "_gr"))Add other variables to summary values
ehLong <-
eh$eh |>
as.data.frame() |>
rownames_to_column("ind0") |>
pivot_longer(starts_with("ais")) |>
mutate(
t = as.numeric(str_match(name, "([0-9]+)")[,1]),
enc = value,
ind = as.numeric(ind0)
) |>
dplyr::select(-c(name, value, ind0))
firstLong <- eh$first |>
as_tibble() |>
rownames_to_column("ind") |>
mutate(ind = as.numeric(ind)) |>
rename(first = value)
lastLong <- eh$last |>
as_tibble() |>
rownames_to_column("ind") |>
mutate(ind = as.numeric(ind)) |>
rename(last = value)
cohortLong <- eh$cohort |>
as_tibble() |>
rownames_to_column("ind") |>
mutate(ind = as.numeric(ind))
weightLong <-
eh$weight |>
as.data.frame() |>
rownames_to_column("ind0") |>
pivot_longer(starts_with("ais")) |>
mutate(
t = as.numeric(str_match(name, "([0-9]+)")[,1]),
weightDATA = value,
ind = as.numeric(ind0)
) |>
dplyr::select(-c(name, value, ind0))
summaryMod1_tt_growth_z <- summaryMod1_tt_growth_z0 |>
left_join(ehLong) |>
mutate(
# meanM1 = mean - 1,
# pSurv = ifelse(meanM1 == 0, 1, 1 - meanM1),
enc8 = ifelse(enc == 1, 0.8, 0)
) |>
left_join(firstLong) |>
left_join(lastLong) |>
left_join(cohortLong) |>
left_join(weightLong) |>
mutate(
resid = weightDATA - mean_weight
) resids <- summaryMod1_tt_growth_z |>
group_by(ind) |>
summarize(
meanResid = mean(abs(resid), na.rm = TRUE),
n = n()
) |>
ungroup()
summaryMod1_tt_growth_zR <-
summaryMod1_tt_growth_z |>
left_join(resids)# write.csv(summaryMod1_tt_growth_zR, './data/outForObservable/summaryMod1_tt_growth_zR.csv')ojs_define(numTags_OJS = dim(eh$tags)[1])
ojs_define(summaryMod1_tt_growth_zR_OJS = (summaryMod1_tt_growth_zR))Select one or more individuals:
viewof selectInd = Inputs.select([...Array(numTags_OJS).keys()], {
label: "Which fish?",
value: 1,
step: 1,
multiple: true
})
summaryMod1_tt_growth_zR_OJS_T = transpose(summaryMod1_tt_growth_zR_OJS)
summaryMod1_tt_growth_zR_OJS_T_selected = summaryMod1_tt_growth_zR_OJS_T.filter((d) =>
selectInd.includes(d.ind)
)Black dots/line is estimated mass and orange dots are observed masses. The green line is the first observation and the red line is the last observation. The number in the upper right corner of each panel is the fish’s cohort. Green dots are estimated growth rates.
Plot.plot({
width: width,
height: 350,
inset: 10,
color: {
scheme: "greys"
},
x: { label: "Age/season combination" },
y: { label: "Body mass (mg)" },
marks: [
Plot.frame(),
Plot.dot(summaryMod1_tt_growth_zR_OJS_T_selected, {
x: "t",
y: "mean_weight"
}),
Plot.line(summaryMod1_tt_growth_zR_OJS_T_selected, {
x: "t",
y: "mean_weight"
}),
Plot.dot(summaryMod1_tt_growth_zR_OJS_T_selected, {
x: "t",
y: "mean_gr",
fill: "green"
}),
Plot.dot(summaryMod1_tt_growth_zR_OJS_T_selected, {
x: "t",
y: "weightDATA",
fill: "orange"
}),
Plot.ruleX(summaryMod1_tt_growth_zR_OJS_T_selected, {
x: "first",
y: 3,
stroke: "green"
}),
Plot.ruleX(summaryMod1_tt_growth_zR_OJS_T_selected, {
x: "last",
y: 3,
stroke: "red"
}),
Plot.text(summaryMod1_tt_growth_zR_OJS_T_selected,
Plot.selectFirst({
x: 1,
y: 60,
frameAnchor: "top-left",
text: (d) => "Cohort = " + d.cohort
})
),
Plot.text(summaryMod1_tt_growth_zR_OJS_T_selected,
Plot.selectFirst({
x: 1,
y: 56,
frameAnchor: "top-left",
text: (d) => "Residual = " + d.meanResid
})
)
],
facet: {
data: summaryMod1_tt_growth_zR_OJS_T_selected,
x: "ind"
}
})Model with phi and p for each age-in-samples (t = column in the encounter history file) and individual (i)
Probability of survival (phi) model structure:
phi(t,i) <- phiInt(t,i)
Probability of capture (p) model structure:
p(t,i) <- pInt(t,i)
Growth rate (gr) model structure:
gr(t,i) <- grInt(t,i)
Survival/growth rate interaction:
Additive
Model code is in ./models/cmrNN_OB/tt_growth/modelCMR_tt_growth_NN_OB_functionsToSource.R
Model is run ‘by hand’ in ./models/cmrNN_OB/tt_growth/modelCMR_tt_growth_NN_OB_makeFile.R
Not using this: Targets are loaded in R/modelCMR_tt_growth_NN_OB.R and saved as modelCMR_tt_growth_NN_OB_target
Model runs:
library(targets)
# Following https://oliviergimenez.github.io/bayesian-cr-workshop/worksheets/4_demo.html
#
modelNum = 2 # phi + growth
#load('./models/cmrNN_OB/tt_growth/runsOut/tt_growth_NN_OB_mostRecent.RData')
load(paste0('./models/cmrNN_OB/tt_growth/runsOut/tt_growth_NN_OB_model', modelNum, '_mostRecent.RData'))
out_NN_OBmod2 <- d
#Input data
eh <- tar_read(eh_OB_2002_2014_target)In the model code, a value of 1 for z or in gamma or omega signifies the individual is alive and a value of 2 signifies the individual is dead. y[i,j] is the observed data (encounter history file).
out_NN_OBmod2$modelCode{
for (i in 1:N) {
weight[i, first[i]] ~ dnorm(3.7, sd = 1.7)
weightDATA[i, first[i]] ~ dnorm(weight[i, first[i]],
sd = 0.2)
for (t in first[i]:(last[i] - 1)) {
weight[i, t + 1] <- weight[i, t] + gr[i, t] * sampleIntervalDATA[i,
t]
weightDATA[i, t + 1] ~ dnorm(weight[i, t + 1], sd = 0.2)
}
}
for (i in 1:N) {
for (t in first[i]:(last[i] - 1)) {
gr[i, t] ~ dnorm(grIntT[t], sd = 0.33)
}
}
for (t in 1:(T - 1)) {
grIntT[t] ~ T(dnorm(0, sd = 1.5), -3.5, 3.5)
grIntTOut[t] <- ilogit(grIntT[t])
}
delta[1] <- 1
delta[2] <- 0
for (i in 1:N) {
for (t in first[i]:(last[i] - 1)) {
gamma[1, 1, t, i] <- phi[i, t]
gamma[1, 2, t, i] <- 1 - phi[i, t]
gamma[2, 1, t, i] <- 0
gamma[2, 2, t, i] <- 1
}
}
for (t in 1:(T - 1)) {
p[t] ~ dunif(0, 1)
omega[1, 1, t] <- 1 - p[t]
omega[1, 2, t] <- p[t]
omega[2, 1, t] <- 1
omega[2, 2, t] <- 0
}
for (i in 1:N) {
for (t in first[i]:(last[i] - 1)) {
logit(phi[i, t]) <- phiInt[i, t]
}
}
for (i in 1:N) {
for (t in first[i]:(last[i] - 1)) {
phiInt[i, t] ~ dnorm(phiIntT[t], sd = 2)
}
}
for (t in 1:(T - 1)) {
phiIntT[t] ~ T(dnorm(0, sd = 1.5), -3.5, 3.5)
phiIntTOut[t] <- ilogit(phiIntT[t])
}
for (i in 1:N) {
z[i, first[i]] ~ dcat(delta[1:2])
for (j in first[i]:(last[i] - 1)) {
z[i, j + 1] ~ dcat(gamma[z[i, j], 1:2, j, i])
yDATA[i, j + 1] ~ dcat(omega[z[i, j + 1], 1:2, j])
}
}
}| nIter | nBurnin | nChains | thinRate |
|---|---|---|---|
| 5000 | 2000 | 2 | 5 |
[1] “Run time = 3.472 hours”
priors <- rnorm(out_NN_OBmod2$runData$nIter * out_NN_OBmod2$runData$nChains, 0, 1000)
MCMCtrace(object = out_NN_OBmod2$mcmc,
#ISB = FALSE,
#exact = TRUE,
params = c("phiIntTOut", "grIntT"),
pdf = FALSE,
priors = priors)
MCMCplot(object = out_NN_OBmod2$mcmc, params = c("phiIntTOut"))
MCMCplot(object = out_NN_OBmod2$mcmc, params = c("p"))
MCMCplot(object = out_NN_OBmod2$mcmc, params = c("grIntT"))
Summary values
#|
(summaryMod2_tt_growth <- MCMCsummary(object = out_NN_OBmod2$mcmc, params = c("phiIntTOut", "p", "grIntT"), round = 3) %>%
rownames_to_column(var = "var")) var mean sd 2.5% 50% 97.5% Rhat n.eff
1 phiIntTOut[1] 0.913 0.017 0.879 0.913 0.944 1.01 50
2 phiIntTOut[2] 0.909 0.021 0.870 0.907 0.953 1.02 22
3 phiIntTOut[3] 0.849 0.023 0.804 0.849 0.892 1.14 43
4 phiIntTOut[4] 0.731 0.029 0.673 0.732 0.782 1.03 58
5 phiIntTOut[5] 0.728 0.031 0.668 0.728 0.793 1.12 35
6 phiIntTOut[6] 0.803 0.042 0.719 0.807 0.877 1.04 13
7 phiIntTOut[7] 0.884 0.054 0.764 0.892 0.956 1.89 8
8 phiIntTOut[8] 0.259 0.042 0.189 0.253 0.353 1.86 27
9 phiIntTOut[9] 0.727 0.053 0.628 0.729 0.821 1.04 23
10 phiIntTOut[10] 0.538 0.085 0.390 0.533 0.709 1.01 12
11 phiIntTOut[11] 0.825 0.115 0.597 0.833 0.970 5.08 9
12 p[1] 0.596 0.021 0.555 0.596 0.636 1.01 180
13 p[2] 0.447 0.019 0.410 0.447 0.484 1.02 163
14 p[3] 0.710 0.019 0.671 0.709 0.748 1.09 224
15 p[4] 0.660 0.021 0.621 0.659 0.701 1.06 162
16 p[5] 0.632 0.023 0.584 0.632 0.678 1.00 201
17 p[6] 0.489 0.029 0.430 0.489 0.544 1.03 91
18 p[7] 0.718 0.042 0.640 0.717 0.808 1.35 67
19 p[8] 0.773 0.049 0.669 0.777 0.861 1.01 365
20 p[9] 0.611 0.062 0.493 0.610 0.727 1.02 439
21 p[10] 0.714 0.081 0.547 0.719 0.857 1.01 301
22 p[11] 0.707 0.139 0.449 0.701 0.970 2.55 44
23 grIntT[1] 1.174 0.028 1.120 1.175 1.226 1.00 177
24 grIntT[2] 1.117 0.022 1.075 1.117 1.159 1.01 177
25 grIntT[3] 9.282 0.029 9.225 9.282 9.339 1.18 46
26 grIntT[4] 3.326 0.024 3.281 3.325 3.375 1.02 175
27 grIntT[5] 0.580 0.036 0.510 0.581 0.653 1.01 80
28 grIntT[6] 1.562 0.034 1.492 1.564 1.625 1.03 89
29 grIntT[7] 17.119 0.048 17.028 17.118 17.209 1.00 60
30 grIntT[8] 7.412 0.058 7.299 7.414 7.523 1.02 71
31 grIntT[9] -1.522 0.079 -1.689 -1.521 -1.375 1.09 54
32 grIntT[10] 4.058 0.099 3.857 4.071 4.252 1.14 31
33 grIntT[11] 22.858 0.135 22.616 22.864 23.098 1.57 19 # To get the mMCMCSummaryRMNA funcion which I adapted to deal with NAs
source('./models/cmrNN_OB/tt_growth/modelCMR_tt_growth_NN_OB_functionsToSource.R')
#summaryMod2_tt_growth_z0 <- MCMCSummaryRMNA(object = out_NN_OBmod2$mcmc, params = c("weightDATA"), round = 3) %>%
summaryMod2_tt_w <- MCMCSummaryRMNA(object = out_NN_OBmod2$mcmc, params = c("weightDATA"), round = 3) %>%
rownames_to_column(var = "var") |>
mutate(
ind0 = str_match(var, "([0-9]+), ([0-9]+)")[,2],
t0 = str_match(var, "([0-9]+), ([0-9]+)")[,3],
ind = as.numeric(ind0),
t = as.numeric(t0)
) |>
dplyr::select(-c(t0, ind0))
summaryMod2_tt_z <- MCMCSummaryRMNA(object = out_NN_OBmod2$mcmc, params = c("z"), round = 3) %>%
rownames_to_column(var = "var") |>
mutate(
ind0 = str_match(var, "([0-9]+), ([0-9]+)")[,2],
t0 = str_match(var, "([0-9]+), ([0-9]+)")[,3],
ind = as.numeric(ind0),
t = as.numeric(t0)
) |>
dplyr::select(-c(t0, ind0))
summaryMod2_tt_z0 <- summaryMod2_tt_z |>
left_join(summaryMod2_tt_w, by = c("ind", "t"), suffix = c("_z", "_weight"))Add other variables to summary values
ehLong <-
eh$eh |>
as.data.frame() |>
rownames_to_column("ind0") |>
pivot_longer(starts_with("ais")) |>
mutate(
t = as.numeric(str_match(name, "([0-9]+)")[,1]),
enc = value,
ind = as.numeric(ind0)
) |>
dplyr::select(-c(name, value, ind0))
firstLong <- eh$first |>
as_tibble() |>
rownames_to_column("ind") |>
mutate(ind = as.numeric(ind)) |>
rename(first = value)
lastLong <- eh$last |>
as_tibble() |>
rownames_to_column("ind") |>
mutate(ind = as.numeric(ind)) |>
rename(last = value)
cohortLong <- eh$cohort |>
as_tibble() |>
rownames_to_column("ind") |>
mutate(ind = as.numeric(ind))
weightLong <-
eh$weight |>
as.data.frame() |>
rownames_to_column("ind0") |>
pivot_longer(starts_with("ais")) |>
mutate(
t = as.numeric(str_match(name, "([0-9]+)")[,1]),
weightDATA = value,
ind = as.numeric(ind0)
) |>
dplyr::select(-c(name, value, ind0))
summaryMod2_tt_z <- summaryMod2_tt_z0 |>
left_join(ehLong) |>
mutate(
# meanM1 = mean - 1,
# pSurv = ifelse(meanM1 == 0, 1, 1 - meanM1),
enc8 = ifelse(enc == 1, 0.8, 0)
) |>
left_join(firstLong) |>
left_join(lastLong) |>
left_join(cohortLong) |>
left_join(weightLong) |>
mutate(
resid = weightDATA - mean_weight
)
# summaryMod2_tt_z <- summaryMod2_tt_z0 |>
# left_join(ehLong) |>
# mutate(
# # meanM1 = mean - 1,
# # pSurv = ifelse(meanM1 == 0, 1, 1 - meanM1),
# enc8 = ifelse(enc == 1, 0.8, 0)
# ) |>
# left_join(firstLong) |>
# left_join(lastLong) |>
# left_join(cohortLong) resids_mod2 <- summaryMod2_tt_z |>
group_by(ind) |>
summarize(
meanResid = mean(abs(resid), na.rm = TRUE),
n = n()
) |>
ungroup()
summaryMod2_tt_zR <-
summaryMod2_tt_z |>
left_join(resids_mod2)ojs_define(numTags_OJS_mod2 = dim(eh$tags)[1])
ojs_define(summaryMod2_tt_zR_OJS = (summaryMod2_tt_zR))
#ojs_define(summary_tt_z_OJS = (summary_tt_z_mod2))Select one or more individuals:
viewof selectInd_mod2 = Inputs.select([...Array(numTags_OJS).keys()], {
label: "Which fish?",
value: 1,
step: 1,
multiple: true
})
summaryMod2_tt_zR_OJS_T = transpose(summaryMod2_tt_zR_OJS)
summaryMod2_tt_zR_OJS_T_selected = summaryMod2_tt_zR_OJS_T.filter((d) =>
selectInd_mod2.includes(d.ind)
)Black dots/line is estimated mass and orange dots are observed masses. The green line is the first observation and the red line is the last observation. The number in the upper right corner of each panel is the fish’s cohort.
Plot.plot({
width: width,
height: 350,
inset: 10,
color: {
scheme: "greys"
},
x: { label: "Age/season combination" },
y: { label: "Body mass (mg)" },
marks: [
Plot.frame(),
Plot.dot(summaryMod2_tt_zR_OJS_T_selected, {
x: "t",
y: "mean_weight"
}),
Plot.line(summaryMod2_tt_zR_OJS_T_selected, {
x: "t",
y: "mean_weight"
}),
<!-- Plot.dot(summaryMod2_tt_zR_OJS_T_selected, { -->
<!-- x: "t", -->
<!-- y: "mean_gr", -->
<!-- fill: "green" -->
<!-- }), -->
Plot.dot(summaryMod2_tt_zR_OJS_T_selected, {
x: "t",
y: "weightDATA",
fill: "orange"
}),
Plot.ruleX(summaryMod2_tt_zR_OJS_T_selected, {
x: "first",
y: 3,
"stroke": "green"
}),
Plot.ruleX(summaryMod2_tt_zR_OJS_T_selected, {
x: "last",
y: 3,
"stroke": "red"
}),
Plot.text(summaryMod2_tt_zR_OJS_T_selected,
Plot.selectFirst({
x: 1,
y: 60,
frameAnchor: "top-left",
text: (d) => "Cohort = " + d.cohort
})
),
Plot.text(summaryMod2_tt_zR_OJS_T_selected,
Plot.selectFirst({
x: 1,
y: 56,
frameAnchor: "top-left",
text: (d) => "Residual = " + d.meanResid
})
)
],
facet: {
data: summaryMod2_tt_zR_OJS_T_selected,
x: "ind"
}
})Black dots/line is estimated probability of survival and orange dots are encountered yes (y = 0.8)/no (y = 0). The green line is the first observation and the red line is the last observation. The number in the upper right corner of each panel is the fish’s cohort.
Plot.plot({
width: width,
height: 350,
inset: 10,
color: {
scheme: "greys"
},
x: { label: "Age/season combination" },
y: { label: "Probability of survival" },
marks: [
Plot.frame(),
Plot.dot(summaryMod2_tt_zR_OJS_T_selected, {
x: "t",
y: "mean_z"
}),
Plot.dot(summaryMod2_tt_zR_OJS_T_selected, {
x: "t",
y: "enc8",
fill: "orange"
}),
Plot.line(summaryMod2_tt_zR_OJS_T_selected, {
x: "t",
y: "mean_z"
}),
Plot.ruleX(summaryMod2_tt_zR_OJS_T_selected, {
x: "first",
y: 1,
"stroke": "green"
}),
Plot.ruleX(summaryMod2_tt_zR_OJS_T_selected, {
x: "last",
y: 1,
"stroke": "red"
})
,
Plot.text(summaryMod2_tt_zR_OJS_T_selected,
Plot.selectFirst({
x: 11,
y: 1,
text: d => d.cohort
})
)
],
facet: {
data: summaryMod2_tt_zR_OJS_T_selected,
x: "ind"
}
}) #write.csv(summary_tt, './models/cmrNN_OB/tt/dataOut/xiaowei/summary_tt.csv')
#write.csv(summary_tt_z, './models/cmrNN_OB/tt/dataOut/xiaowei/summary_tt_z.csv')